Using Behavior Cloning to Play HumanoidBulletEnv-v0

PyTorch version

import sys
import logging
import itertools
import time

import numpy as np
np.random.seed(0)
import pandas as pd
import gym
import pybullet_envs
import torch
torch.manual_seed(0)
import torch.optim as optim
import torch.nn as nn

logging.basicConfig(level=logging.DEBUG,
        format='%(asctime)s [%(levelname)s] %(message)s',
        stream=sys.stdout, datefmt='%H:%M:%S')

Environment

env = gym.make("HumanoidBulletEnv-v0")
env.seed(0)
for key in vars(env):
    logging.info('%s: %s', key, vars(env)[key])
for key in vars(env.spec):
    logging.info('%s: %s', key, vars(env.spec)[key])

00:02:33 [INFO] env: <HumanoidBulletEnv<HumanoidBulletEnv-v0>>
00:02:33 [INFO] action_space: Box(-1.0, 1.0, (17,), float32)
00:02:33 [INFO] observation_space: Box(-inf, inf, (44,), float32)
00:02:33 [INFO] reward_range: (-inf, inf)
00:02:33 [INFO] metadata: {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 60}
00:02:33 [INFO] _max_episode_steps: 1000
00:02:33 [INFO] _elapsed_steps: None
00:02:33 [INFO] id: HumanoidBulletEnv-v0
00:02:33 [INFO] entry_point: pybullet_envs.gym_locomotion_envs:HumanoidBulletEnv
00:02:33 [INFO] reward_threshold: None
00:02:33 [INFO] nondeterministic: False
00:02:33 [INFO] max_episode_steps: 1000
00:02:33 [INFO] _kwargs: {}
00:02:33 [INFO] _env_name: HumanoidBulletEnv

Agent

class ExpertAgent:
    def reset(self, mode):
        pass

    def step(self, observation, reward, done):
        weights0 = np.array([
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                [-0.0193, -0.1029, +0.1858, +0.0139, +0.2608, -0.0146, +0.0154, +0.0489, -0.1093, +0.0104, -0.2219, +0.0648, -0.0656, -0.0877, +0.0203, -0.0899, -0.0903],
                [-0.0886, +0.1405, -0.3229, +0.1365, -0.0121, +0.0062, -0.0392, -0.0051, +0.1538, +0.2586, -0.0580, +0.1502, -0.0022, -0.0271, -0.0936, -0.4547, +0.0754],
                [+0.3475, +0.0164, -0.2164, +0.0795, -0.0293, +0.1575, +0.0395, -0.4194, +0.0051, -0.0399, +0.1034, +0.3149, -0.0858, -0.1606, -0.0197, -0.0434, -0.0950],
                [+0.1236, -0.0872, -0.2009, +0.3143, +0.1047, +0.0123, -0.1373, -0.0199, +0.0415, -0.0361, +0.4936, +0.1017, +0.0232, -0.2103, -0.0957, -0.1162, +0.0018],
                [-0.0303, +0.2271, +0.2671, -0.1139, +0.0505, +0.0459, +0.0181, -0.0978, +0.0442, +0.0428, -0.0411, +0.1537, -0.2310, -0.0133, +0.0393, +0.2811, +0.0302],
                [+0.0180, -0.2184, -0.0850, -0.1738, -0.1618, +0.0298, -0.0198, +0.3145, -0.1872, -0.0837, -0.0753, -0.1052, -0.0610, -0.1089, -0.0288, +0.0058, -0.0733],
                [+0.0005, +0.1507, +0.1555, +0.0516, -0.1457, -0.1738, -0.0570, +0.0876, +0.2975, +0.1416, +0.0454, -0.3029, +0.0148, -0.2664, +0.0913, +0.0374, +0.1604],
                [+0.0099, +0.1211, +0.3735, +0.2346, +0.0826, -0.0476, -0.0548, -0.0957, +0.0246, -0.0414, +0.0233, -0.0779, +0.0275, -0.1103, +0.1439, -0.1177, +0.0950],
                [+0.0991, -0.0663, -0.0226, -0.2599, -0.2676, -0.2060, -0.0898, +0.0218, -0.0629, +0.3991, -0.0857, -0.0919, -0.0442, +0.0097, -0.0634, -0.1714, +0.0120],
                [-0.0083, -0.2737, -0.0861, -0.1251, +0.0406, +0.0368, -0.1133, +0.1224, -0.1233, +0.0078, +0.1233, +0.0664, -0.2030, +0.0159, -0.4872, +0.0998, -0.1442],
                [-0.0557, +0.0044, +0.0650, -0.3940, +0.0112, +0.1438, +0.0244, -0.0566, -0.1197, +0.0153, -0.0472, -0.1162, -0.1096, -0.0067, +0.0748, -0.1948, +0.0299],
                [-0.1223, -0.1968, +0.0289, -0.1279, +0.2201, -0.2378, +0.0276, +0.2640, +0.0479, +0.0962, -0.0191, -0.2616, -0.1661, -0.1301, +0.1400, +0.0645, -0.1336],
                [+0.0914, +0.0268, +0.0498, +0.2482, -0.1405, -0.1721, +0.0066, +0.0427, +0.1062, -0.0699, +0.0238, +0.1893, +0.0670, -0.0401, -0.0636, +0.0611, -0.0596],
                [-0.1982, -0.2318, +0.1808, +0.0593, +0.1440, +0.3635, +0.0442, -0.1524, +0.0650, +0.0216, -0.0772, -0.1202, +0.2329, +0.0204, -0.0736, +0.2445, +0.0261],
                [-0.3588, +0.0298, -0.0034, -0.0458, +0.2777, +0.2485, +0.3801, -0.0695, -0.1679, -0.5362, -0.1887, +0.0389, +0.2512, -0.0413, +0.0365, -0.0915, +0.0519],
                [-0.0069, +0.1737, +0.1234, +0.0086, +0.0216, +0.1098, -0.0172, +0.0209, -0.1796, +0.1730, -0.0681, +0.1485, +0.1096, -0.0660, -0.0895, +0.1351, -0.0554],
                [-0.2458, +0.0149, +0.0155, -0.0099, +0.0206, +0.3378, +0.2285, +0.0385, -0.0532, +0.2575, -0.0545, +0.0151, +0.0540, -0.0833, +0.0946, +0.0446, -0.0862],
                [-0.1136, +0.1565, +0.1244, -0.0161, -0.0614, +0.1204, -0.0771, +0.2362, +0.0355, +0.2028, -0.0155, -0.0652, -0.2262, -0.0749, -0.1730, -0.0957, +0.2760],
                [+0.0634, -0.0011, -0.1770, -0.1426, +0.1599, +0.0892, -0.0261, +0.0790, +0.0812, +0.4281, +0.0143, +0.1554, +0.3770, -0.0206, -0.1564, +0.0818, -0.0514],
                [+0.0102, -0.1232, -0.2659, -0.1866, +0.0090, +0.2672, -0.0540, +0.1100, -0.2942, -0.2454, -0.0566, -0.2273, +0.1938, +0.0229, -0.1563, -0.0024, -0.0975],
                [-0.0645, -0.0004, +0.1548, +0.2129, -0.0429, -0.0628, +0.0306, +0.1769, -0.0491, -0.0285, -0.0621, -0.4293, +0.1821, +0.1154, -0.0027, +0.0264, -0.0107],
                [+0.3158, -0.0591, +0.0461, +0.0682, +0.0015, -0.0667, +0.1125, +0.0242, -0.0998, +0.0405, -0.1104, +0.0892, -0.1950, -0.0443, -0.1472, +0.1181, -0.0764],
                [-0.0697, -0.0853, +0.1715, +0.0623, +0.0484, -0.0205, -0.2043, -0.0463, -0.1115, +0.0058, -0.0556, -0.0808, +0.0454, -0.0959, +0.1666, +0.3646, +0.2677],
                [-0.1574, -0.2403, -0.1285, -0.0211, -0.2054, -0.0027, +0.1099, -0.1112, -0.2730, -0.1247, +0.1788, -0.1336, -0.1028, +0.1764, +0.2594, -0.0231, +0.1732],
                [+0.2124, +0.1167, -0.0895, -0.0324, +0.2043, +0.0680, -0.0162, -0.0062, -0.0897, +0.0854, +0.0748, +0.3693, -0.0414, +0.0972, +0.0094, +0.0354, +0.1289],
                [+0.2460, -0.1170, -0.1432, -0.0896, +0.0093, +0.0557, -0.0195, +0.0088, +0.0839, +0.0375, +0.1659, -0.0451, -0.1184, -0.2017, +0.0481, -0.1517, +0.1372],
                [-0.0602, +0.3316, -0.0452, -0.0173, +0.0148, +0.0459, +0.0167, -0.0842, +0.0428, +0.2873, -0.0527, +0.0752, -0.0441, +0.0583, +0.0443, +0.1112, -0.0272],
                [+0.0051, -0.0555, +0.1717, +0.1398, -0.1614, -0.0300, -0.1820, -0.0658, +0.0742, -0.0847, -0.1139, +0.0728, +0.1420, +0.2099, -0.2902, +0.1338, +0.1417],
                [-0.0094, -0.0020, -0.1126, -0.2572, -0.2766, +0.0049, +0.1369, +0.0054, -0.1328, +0.0361, +0.1361, +0.3090, +0.0047, +0.1695, +0.0896, -0.1901, -0.1786],
                [+0.1339, -0.0886, +0.0770, +0.0189, -0.1017, -0.0070, +0.1551, -0.0489, +0.0966, -0.0131, -0.0856, -0.1183, +0.1585, -0.0139, +0.1861, -0.1487, +0.0005],
                [+0.2260, +0.0098, -0.3146, -0.0441, +0.1948, +0.0520, +0.0863, -0.0021, -0.1183, +0.0328, +0.0124, +0.0056, +0.0944, -0.0319, +0.0679, +0.0774, +0.0409],
                [-0.0237, -0.3453, -0.1483, -0.2272, +0.1675, -0.1041, -0.0126, +0.3376, -0.0138, +0.1525, -0.0322, +0.2070, -0.2725, -0.0600, -0.1466, -0.2563, +0.0551],
                [+0.1966, -0.0838, -0.0537, +0.0933, -0.0060, -0.0721, -0.0399, -0.1153, -0.1987, -0.0413, +0.2588, +0.0648, -0.0473, +0.1387, +0.6180, +0.0229, -0.2610],
                [-0.1566, -0.0589, -0.0600, +0.0966, +0.0738, -0.0744, -0.0010, +0.0253, +0.2387, -0.0539, +0.0625, +0.1197, +0.0486, +0.0605, +0.2775, -0.0594, -0.0759],
                [-0.4288, -0.0693, +0.0067, +0.3567, +0.0813, -0.1369, -0.3431, +0.0559, -0.0320, +0.0158, -0.1947, -0.3235, +0.1294, -0.2915, -0.2301, -0.1980, +0.0892],
                [-0.1741, +0.0445, -0.0525, +0.0650, -0.0474, +0.1845, -0.0831, -0.0693, -0.0071, -0.0772, +0.0915, -0.0571, -0.1035, -0.0038, -0.1508, -0.2323, -0.1788],
                [+0.1543, -0.0994, +0.0674, +0.1953, +0.1254, -0.1263, +0.0854, -0.1048, +0.0106, -0.0203, +0.1019, -0.0881, +0.2788, -0.2237, -0.0723, +0.0393, -0.0969],
                [+0.0560, +0.2425, +0.1031, +0.1155, -0.1187, -0.1155, -0.0789, +0.2113, +0.0024, +0.2648, -0.1811, -0.0259, -0.1363, -0.2149, -0.2199, -0.1105, -0.1616],
                [-0.0967, +0.0007, -0.2230, +0.1197, +0.1085, +0.0267, -0.0633, +0.1611, -0.0078, +0.0088, -0.0879, -0.1362, +0.1364, -0.1784, +0.1725, -0.0491, -0.0177],
                [+0.0724, -0.0574, +0.0696, -0.0095, -0.1601, +0.1243, +0.2373, -0.1058, +0.1610, -0.1874, -0.0689, +0.1806, -0.2117, -0.0855, -0.0657, +0.0366, -0.0797],
                ])
        bias2 = np.array([-0.0356, +0.0776, -0.0344, +0.1375, +0.1048, +0.3648, +0.3240, +0.1319, +0.1161, +0.3373, +0.3193, +0.0120, +0.0253, -0.2434, -0.1291, +0.1042, -0.2448])

        hidden1 = (np.dot(observation, weights0) + bias0).clip(min=0)
        hidden2 = (np.dot(hidden1, weights1) + bias1).clip(min=0)
        action = np.dot(hidden2, weights2) + bias2
        return action

    def close(self):
        pass


expert_agent = ExpertAgent()

class SAReplayer:
    def __init__(self):
        self.fields = ['state', 'action']
        self.data = {field: [] for field in self.fields}
        self.memory = pd.DataFrame()

    def store(self, *args):
        for field, arg in zip(self.fields, args):
            self.data[field].append(arg)

    def sample(self, size=None):
        if len(self.memory) < len(self.data[self.fields[0]]):
            self.memory = pd.DataFrame(self.data, columns=self.fields)
        if size is None:
            indices = self.memory.index
        else:
            indices = np.random.choice(self.memory.index, size=size)
        return (np.stack(self.memory.loc[indices, field]) for field in
                self.fields)

class BCAgent:
    def __init__(self, env, expert_agent):
        self.expert_agent = expert_agent

        state_dim = env.observation_space.shape[0]
        action_dim = env.action_space.shape[0]

        self.net = self.build_net(input_size=state_dim,
                hidden_sizes=[256, 128], output_size=action_dim)
        self.loss = nn.MSELoss()
        self.optimizer = optim.Adam(self.net.parameters())

    def build_net(self, input_size, hidden_sizes, output_size=1,
            output_activator=None):
        layers = []
        for input_size, output_size in zip(
                [input_size,] + hidden_sizes, hidden_sizes + [output_size,]):
            layers.append(nn.Linear(input_size, output_size))
            layers.append(nn.ReLU())
        layers = layers[:-1]
        if output_activator:
            layers.append(output_activator)
        net = nn.Sequential(*layers)
        return net

    def reset(self, mode=None):
        self.mode = mode
        if self.mode == 'expert':
            self.expert_agent.reset(mode)
            self.expert_replayer = SAReplayer()

    def step(self, observation, reward, done):
        if self.mode == 'expert':
            action = expert_agent.step(observation, reward, done)
            self.expert_replayer.store(observation, action)
        else:
            state_tensor = torch.as_tensor(observation, dtype=torch.float
                    ).unsqueeze(0)
            action_tensor = self.net(state_tensor)
            action = action_tensor.detach().numpy()[0]
        return action

    def close(self):
        if self.mode == 'expert':
            self.expert_agent.close()
            for _ in range(10):
                self.learn()

    def learn(self):
        states, actions = self.expert_replayer.sample(1024)
        state_tensor = torch.as_tensor(states, dtype=torch.float)
        action_tensor = torch.as_tensor(actions, dtype=torch.float)

        pred_tensor = self.net(state_tensor)
        loss_tensor = self.loss(pred_tensor, action_tensor)
        self.optimizer.zero_grad()
        loss_tensor.backward()
        self.optimizer.step()



agent = BCAgent(env, expert_agent)

def play_episode(env, agent, max_episode_steps=None, mode=None, render=False):
    observation, reward, done = env.reset(), 0., False
    agent.reset(mode=mode)
    episode_reward, elapsed_steps = 0., 0
    while True:
        action = agent.step(observation, reward, done)
        if render:
            env.render()
        if done:
            break
        observation, reward, done, _ = env.step(action)
        episode_reward += reward
        elapsed_steps += 1
        if max_episode_steps and elapsed_steps >= max_episode_steps:
            break
    agent.close()
    return episode_reward, elapsed_steps



logging.info('==== expert ====')
episode_rewards = []
for episode in range(100):
    episode_reward, elapsed_steps = play_episode(env, agent, mode='expert')
    episode_rewards.append(episode_reward)
    logging.debug('expert episode %d: reward = %.2f, steps = %d',
            episode, episode_reward, elapsed_steps)
logging.info('average expert episode reward = %.2f ± %.2f',
        np.mean(episode_rewards), np.std(episode_rewards))


logging.info('==== test ====')
episode_rewards = []
for episode in range(100):
    episode_reward, elapsed_steps = play_episode(env, agent)
    episode_rewards.append(episode_reward)
    logging.debug('test episode %d: reward = %.2f, steps = %d',
            episode, episode_reward, elapsed_steps)
logging.info('average episode reward = %.2f ± %.2f',
        np.mean(episode_rewards), np.std(episode_rewards))

00:02:33 [INFO] ==== expert ====
00:02:44 [DEBUG] expert episode 0: reward = 3636.76, steps = 1000
00:02:55 [DEBUG] expert episode 1: reward = 3584.22, steps = 1000
00:03:06 [DEBUG] expert episode 2: reward = 3582.76, steps = 1000
00:03:17 [DEBUG] expert episode 3: reward = 3641.40, steps = 1000
00:03:28 [DEBUG] expert episode 4: reward = 3581.67, steps = 1000
00:03:39 [DEBUG] expert episode 5: reward = 3597.90, steps = 1000
00:03:50 [DEBUG] expert episode 6: reward = 3576.58, steps = 1000
00:04:01 [DEBUG] expert episode 7: reward = 3627.58, steps = 1000
00:04:13 [DEBUG] expert episode 8: reward = 3585.69, steps = 1000
00:04:24 [DEBUG] expert episode 9: reward = 3612.16, steps = 1000
00:04:36 [DEBUG] expert episode 10: reward = 3534.30, steps = 1000
00:04:50 [DEBUG] expert episode 11: reward = 3621.15, steps = 1000
00:04:51 [DEBUG] expert episode 12: reward = 19.42, steps = 67
00:05:05 [DEBUG] expert episode 13: reward = 3595.52, steps = 1000
00:05:17 [DEBUG] expert episode 14: reward = 3612.40, steps = 1000
00:05:28 [DEBUG] expert episode 15: reward = 3607.04, steps = 1000
00:05:39 [DEBUG] expert episode 16: reward = 3626.01, steps = 1000
00:05:50 [DEBUG] expert episode 17: reward = 3628.98, steps = 1000
00:05:50 [DEBUG] expert episode 18: reward = -3.76, steps = 22
00:06:02 [DEBUG] expert episode 19: reward = 3621.28, steps = 1000
00:06:12 [DEBUG] expert episode 20: reward = 3562.74, steps = 1000
00:06:13 [DEBUG] expert episode 21: reward = -10.92, steps = 29
00:06:24 [DEBUG] expert episode 22: reward = 3626.82, steps = 1000
00:06:25 [DEBUG] expert episode 23: reward = 7.51, steps = 49
00:06:35 [DEBUG] expert episode 24: reward = 3637.81, steps = 1000
00:06:47 [DEBUG] expert episode 25: reward = 3594.70, steps = 1000
00:06:58 [DEBUG] expert episode 26: reward = 3583.24, steps = 1000
00:07:08 [DEBUG] expert episode 27: reward = 3637.90, steps = 1000
00:07:09 [DEBUG] expert episode 28: reward = 61.78, steps = 79
00:07:20 [DEBUG] expert episode 29: reward = 3604.58, steps = 1000
00:07:31 [DEBUG] expert episode 30: reward = 3611.99, steps = 1000
00:07:42 [DEBUG] expert episode 31: reward = 3638.35, steps = 1000
00:07:52 [DEBUG] expert episode 32: reward = 3584.59, steps = 1000
00:08:03 [DEBUG] expert episode 33: reward = 3612.45, steps = 1000
00:08:14 [DEBUG] expert episode 34: reward = 3639.87, steps = 1000
00:08:15 [DEBUG] expert episode 35: reward = -4.10, steps = 37
00:08:26 [DEBUG] expert episode 36: reward = 3610.86, steps = 1000
00:08:36 [DEBUG] expert episode 37: reward = 3627.18, steps = 1000
00:08:47 [DEBUG] expert episode 38: reward = 3625.77, steps = 1000
00:08:58 [DEBUG] expert episode 39: reward = 3602.48, steps = 1000
00:09:08 [DEBUG] expert episode 40: reward = 3513.75, steps = 1000
00:09:19 [DEBUG] expert episode 41: reward = 3616.72, steps = 1000
00:09:20 [DEBUG] expert episode 42: reward = 3.92, steps = 33
00:09:31 [DEBUG] expert episode 43: reward = 3610.82, steps = 1000
00:09:42 [DEBUG] expert episode 44: reward = 3590.80, steps = 1000
00:09:53 [DEBUG] expert episode 45: reward = 3598.02, steps = 1000
00:10:04 [DEBUG] expert episode 46: reward = 3584.06, steps = 1000
00:10:16 [DEBUG] expert episode 47: reward = 3631.53, steps = 1000
00:10:27 [DEBUG] expert episode 48: reward = 3635.42, steps = 1000
00:10:38 [DEBUG] expert episode 49: reward = 3614.20, steps = 1000
00:10:49 [DEBUG] expert episode 50: reward = 3616.14, steps = 1000
00:11:00 [DEBUG] expert episode 51: reward = 3561.36, steps = 1000
00:11:11 [DEBUG] expert episode 52: reward = 3597.01, steps = 1000
00:11:22 [DEBUG] expert episode 53: reward = 3599.24, steps = 1000
00:11:33 [DEBUG] expert episode 54: reward = 3619.03, steps = 1000
00:11:44 [DEBUG] expert episode 55: reward = 3627.83, steps = 1000
00:11:55 [DEBUG] expert episode 56: reward = 3626.63, steps = 1000
00:12:06 [DEBUG] expert episode 57: reward = 3595.42, steps = 1000
00:12:18 [DEBUG] expert episode 58: reward = 3608.29, steps = 1000
00:12:19 [DEBUG] expert episode 59: reward = 32.05, steps = 86
00:12:30 [DEBUG] expert episode 60: reward = 3555.76, steps = 1000
00:12:41 [DEBUG] expert episode 61: reward = 3608.93, steps = 1000
00:12:52 [DEBUG] expert episode 62: reward = 3618.58, steps = 1000
00:13:03 [DEBUG] expert episode 63: reward = 3628.57, steps = 1000
00:13:14 [DEBUG] expert episode 64: reward = 3638.96, steps = 1000
00:13:25 [DEBUG] expert episode 65: reward = 3617.05, steps = 1000
00:13:36 [DEBUG] expert episode 66: reward = 3598.20, steps = 1000
00:13:47 [DEBUG] expert episode 67: reward = 3445.20, steps = 1000
00:13:58 [DEBUG] expert episode 68: reward = 3611.77, steps = 1000
00:14:09 [DEBUG] expert episode 69: reward = 3607.09, steps = 1000
00:14:20 [DEBUG] expert episode 70: reward = 3621.62, steps = 1000
00:14:31 [DEBUG] expert episode 71: reward = 3602.90, steps = 1000
00:14:42 [DEBUG] expert episode 72: reward = 3530.39, steps = 1000
00:14:53 [DEBUG] expert episode 73: reward = 3603.60, steps = 1000
00:14:54 [DEBUG] expert episode 74: reward = 50.15, steps = 77
00:15:05 [DEBUG] expert episode 75: reward = 3592.32, steps = 1000
00:15:16 [DEBUG] expert episode 76: reward = 3620.81, steps = 1000
00:15:27 [DEBUG] expert episode 77: reward = 3611.51, steps = 1000
00:15:38 [DEBUG] expert episode 78: reward = 3599.40, steps = 1000
00:15:49 [DEBUG] expert episode 79: reward = 3632.42, steps = 1000
00:16:00 [DEBUG] expert episode 80: reward = 3602.78, steps = 1000
00:16:11 [DEBUG] expert episode 81: reward = 3646.05, steps = 1000
00:16:22 [DEBUG] expert episode 82: reward = 3609.66, steps = 1000
00:16:33 [DEBUG] expert episode 83: reward = 3606.29, steps = 1000
00:16:45 [DEBUG] expert episode 84: reward = 3601.71, steps = 1000
00:16:45 [DEBUG] expert episode 85: reward = -1.99, steps = 34
00:16:56 [DEBUG] expert episode 86: reward = 3595.97, steps = 1000
00:17:07 [DEBUG] expert episode 87: reward = 3620.52, steps = 1000
00:17:18 [DEBUG] expert episode 88: reward = 3601.69, steps = 1000
00:17:29 [DEBUG] expert episode 89: reward = 3594.18, steps = 1000
00:17:40 [DEBUG] expert episode 90: reward = 3460.20, steps = 1000
00:17:51 [DEBUG] expert episode 91: reward = 3619.25, steps = 1000
00:18:02 [DEBUG] expert episode 92: reward = 3619.31, steps = 1000
00:18:13 [DEBUG] expert episode 93: reward = 3417.55, steps = 1000
00:18:25 [DEBUG] expert episode 94: reward = 3615.89, steps = 1000
00:18:35 [DEBUG] expert episode 95: reward = 3591.82, steps = 1000
00:18:46 [DEBUG] expert episode 96: reward = 3623.42, steps = 1000
00:18:57 [DEBUG] expert episode 97: reward = 3614.98, steps = 1000
00:19:08 [DEBUG] expert episode 98: reward = 3581.44, steps = 1000
00:19:19 [DEBUG] expert episode 99: reward = 3579.60, steps = 1000
00:19:19 [INFO] average expert episode reward = 3242.22 ± 1076.24
00:19:19 [INFO] ==== test ====
00:19:19 [DEBUG] test episode 0: reward = 109.70, steps = 55
00:19:20 [DEBUG] test episode 1: reward = 93.02, steps = 51
00:19:20 [DEBUG] test episode 2: reward = 75.39, steps = 65
00:19:20 [DEBUG] test episode 3: reward = 102.55, steps = 54
00:19:21 [DEBUG] test episode 4: reward = 86.29, steps = 45
00:19:21 [DEBUG] test episode 5: reward = 88.55, steps = 49
00:19:21 [DEBUG] test episode 6: reward = 99.22, steps = 54
00:19:22 [DEBUG] test episode 7: reward = 99.89, steps = 51
00:19:22 [DEBUG] test episode 8: reward = 97.62, steps = 55
00:19:22 [DEBUG] test episode 9: reward = 105.89, steps = 56
00:19:23 [DEBUG] test episode 10: reward = 82.77, steps = 46
00:19:23 [DEBUG] test episode 11: reward = 110.19, steps = 64
00:19:23 [DEBUG] test episode 12: reward = 61.94, steps = 51
00:19:23 [DEBUG] test episode 13: reward = -6.18, steps = 24
00:19:23 [DEBUG] test episode 14: reward = -1.88, steps = 18
00:19:24 [DEBUG] test episode 15: reward = 78.00, steps = 46
00:19:24 [DEBUG] test episode 16: reward = 110.00, steps = 62
00:19:25 [DEBUG] test episode 17: reward = 156.67, steps = 90
00:19:25 [DEBUG] test episode 18: reward = 107.54, steps = 56
00:19:25 [DEBUG] test episode 19: reward = 68.56, steps = 60
00:19:26 [DEBUG] test episode 20: reward = 93.51, steps = 50
00:19:26 [DEBUG] test episode 21: reward = 85.47, steps = 48
00:19:26 [DEBUG] test episode 22: reward = 95.60, steps = 53
00:19:26 [DEBUG] test episode 23: reward = 96.80, steps = 52
00:19:27 [DEBUG] test episode 24: reward = 107.31, steps = 67
00:19:27 [DEBUG] test episode 25: reward = -48.03, steps = 20
00:19:27 [DEBUG] test episode 26: reward = 84.42, steps = 47
00:19:27 [DEBUG] test episode 27: reward = 87.39, steps = 49
00:19:28 [DEBUG] test episode 28: reward = 76.35, steps = 40
00:19:28 [DEBUG] test episode 29: reward = 88.63, steps = 47
00:19:28 [DEBUG] test episode 30: reward = 44.59, steps = 38
00:19:29 [DEBUG] test episode 31: reward = 121.62, steps = 68
00:19:29 [DEBUG] test episode 32: reward = 90.10, steps = 47
00:19:29 [DEBUG] test episode 33: reward = 79.29, steps = 43
00:19:29 [DEBUG] test episode 34: reward = 103.21, steps = 53
00:19:30 [DEBUG] test episode 35: reward = 87.00, steps = 48
00:19:30 [DEBUG] test episode 36: reward = 176.82, steps = 87
00:19:30 [DEBUG] test episode 37: reward = 82.27, steps = 43
00:19:31 [DEBUG] test episode 38: reward = 86.58, steps = 46
00:19:31 [DEBUG] test episode 39: reward = 105.01, steps = 84
00:19:32 [DEBUG] test episode 40: reward = 118.70, steps = 75
00:19:32 [DEBUG] test episode 41: reward = 117.10, steps = 58
00:19:32 [DEBUG] test episode 42: reward = 129.58, steps = 101
00:19:33 [DEBUG] test episode 43: reward = 133.95, steps = 67
00:19:33 [DEBUG] test episode 44: reward = 4.78, steps = 35
00:19:33 [DEBUG] test episode 45: reward = 139.70, steps = 75
00:19:34 [DEBUG] test episode 46: reward = 121.71, steps = 65
00:19:34 [DEBUG] test episode 47: reward = 129.57, steps = 75
00:19:34 [DEBUG] test episode 48: reward = 47.75, steps = 42
00:19:35 [DEBUG] test episode 49: reward = 57.99, steps = 47
00:19:35 [DEBUG] test episode 50: reward = 96.12, steps = 56
00:19:35 [DEBUG] test episode 51: reward = 78.11, steps = 45
00:19:36 [DEBUG] test episode 52: reward = 97.64, steps = 58
00:19:36 [DEBUG] test episode 53: reward = 164.66, steps = 76
00:19:36 [DEBUG] test episode 54: reward = 11.92, steps = 24
00:19:37 [DEBUG] test episode 55: reward = 140.69, steps = 92
00:19:37 [DEBUG] test episode 56: reward = 123.83, steps = 60
00:19:37 [DEBUG] test episode 57: reward = 130.41, steps = 72
00:19:38 [DEBUG] test episode 58: reward = 99.72, steps = 52
00:19:38 [DEBUG] test episode 59: reward = 77.24, steps = 61
00:19:38 [DEBUG] test episode 60: reward = 105.45, steps = 56
00:19:39 [DEBUG] test episode 61: reward = 104.27, steps = 57
00:19:39 [DEBUG] test episode 62: reward = 123.68, steps = 86
00:19:39 [DEBUG] test episode 63: reward = 22.04, steps = 50
00:19:40 [DEBUG] test episode 64: reward = 86.43, steps = 47
00:19:40 [DEBUG] test episode 65: reward = 104.10, steps = 57
00:19:40 [DEBUG] test episode 66: reward = 113.42, steps = 56
00:19:41 [DEBUG] test episode 67: reward = 105.19, steps = 58
00:19:41 [DEBUG] test episode 68: reward = 150.21, steps = 99
00:19:42 [DEBUG] test episode 69: reward = 55.50, steps = 54
00:19:42 [DEBUG] test episode 70: reward = 75.83, steps = 40
00:19:42 [DEBUG] test episode 71: reward = 96.37, steps = 71
00:19:43 [DEBUG] test episode 72: reward = 91.95, steps = 80
00:19:43 [DEBUG] test episode 73: reward = 154.82, steps = 75
00:19:43 [DEBUG] test episode 74: reward = 127.42, steps = 82
00:19:44 [DEBUG] test episode 75: reward = 87.33, steps = 66
00:19:44 [DEBUG] test episode 76: reward = 83.82, steps = 44
00:19:44 [DEBUG] test episode 77: reward = 84.53, steps = 64
00:19:45 [DEBUG] test episode 78: reward = 93.76, steps = 49
00:19:45 [DEBUG] test episode 79: reward = 71.93, steps = 41
00:19:45 [DEBUG] test episode 80: reward = 64.22, steps = 46
00:19:46 [DEBUG] test episode 81: reward = 97.67, steps = 53
00:19:46 [DEBUG] test episode 82: reward = 78.11, steps = 44
00:19:46 [DEBUG] test episode 83: reward = 85.58, steps = 45
00:19:46 [DEBUG] test episode 84: reward = 87.09, steps = 62
00:19:47 [DEBUG] test episode 85: reward = 154.37, steps = 88
00:19:47 [DEBUG] test episode 86: reward = 100.67, steps = 51
00:19:47 [DEBUG] test episode 87: reward = 91.01, steps = 49
00:19:48 [DEBUG] test episode 88: reward = 95.02, steps = 52
00:19:48 [DEBUG] test episode 89: reward = 98.97, steps = 56
00:19:49 [DEBUG] test episode 90: reward = 132.47, steps = 75
00:19:49 [DEBUG] test episode 91: reward = -2.26, steps = 43
00:19:49 [DEBUG] test episode 92: reward = 78.13, steps = 42
00:19:50 [DEBUG] test episode 93: reward = 136.35, steps = 92
00:19:50 [DEBUG] test episode 94: reward = 129.32, steps = 74
00:19:50 [DEBUG] test episode 95: reward = 122.33, steps = 64
00:19:51 [DEBUG] test episode 96: reward = 84.55, steps = 46
00:19:51 [DEBUG] test episode 97: reward = 183.28, steps = 100
00:19:52 [DEBUG] test episode 98: reward = 110.42, steps = 61
00:19:52 [DEBUG] test episode 99: reward = 81.12, steps = 45
00:19:52 [INFO] average episode reward = 94.37 ± 37.49

env.close()